Towards foundation models of biological image segmentation
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MedSAM provides a successful paradigm for adapting natural image foundation models to new domains.
“Additionally, MedSAM provides a successful paradigm for adapting natural image foundation models to new domains, which can be further extended to biological image segmentation30, such as cell segmentation in light microscopy images31 and organelle segmentation in electron microscopy images32.”
From Segment anything in medical images (2024) · cites this paper as [30]
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References 17 · Cited by 57
- Authors
- Ma, J., & Wang, B.
- Affiliations
- University Health Network (CA)
- Corresponding author
- Wang, Bo
- Venue
- Nature Methods
- Published
- 2023-07
- Volume
- 20
- Issue
- 7
- Pages
- 953-955
- ISSN
- 1548-7091 (ISSN-L)
- 1548-7105
- OpenAlex
- W4383904219
- Cited by
- 57
- References
- 17
- Citations by year
- 2026: 14 · 2025: 18 · 2024: 21 · 2023: 4
- FWCI
- 6.92
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Nature Methods (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada (NSERC, RGPIN-2020-06189 and DGECR-2020-00294)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Biophysics › Cell Image Analysis Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- foundation models, bioimaging, imaging modalities, multimodal segmentation
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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